Deep Learning: an Integrative Systematic Review of Its Applications in Mapping Using UAV Imagery
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The advances in Deep Learning (DL) techniques have expanded the use of Unmanned Aerial Vehicles (UAVs) for cartographic mapping and remote sensing, enhancing automation and the accuracy of geospatial products. Given the rapid growth of such applications, this article aims to systematize and critically analyze research integrating DL and UAV imagery in the mapping context, focusing on the main neural network architectures, sensors, and application domains. An integrative systematic review was conducted using the Web of Science, Scopus, and ScienceDirect databases, covering the period from 2020 to 2025. The screening process resulted in 22 selected studies, grouped into five thematic categories: agriculture, object detection, inspections, wildfires, and LiDAR. The findings highlight the predominance of YOLO and U-Net architectures, the increasing use of multispectral and thermal data, and the lack of methodological standardization in training and validation processes. The integrative analysis revealed trends, gaps, and ethical and technical challenges in applying DL with UAV imagery for mapping purposes. This research contributes to consolidating technical and scientific knowledge in this field and reinforces the need for standardized protocols and practices in the development of AI-based cartographic products.
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